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<span class="breadcrumb-node">查询语句提升权重</span>
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<div class="titlepage"><div><div>
<h2 class="title">
<a id="_boosting_query_clauses"></a>查询语句提升权重<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elasticsearch-cn/elasticsearch-definitive-guide/edit/cn/100_Full_Text_Search/25_Boosting_clauses.asciidoc">edit</a>
</h2>
</div></div></div>
<p>当然 <code class="literal">bool</code> 查询不仅限于组合简单的单个词 <code class="literal">match</code> 查询，它可以组合任意其他的查询，以及其他 <code class="literal">bool</code> 查询。普遍的用法是通过汇总多个独立查询的分数，从而达到为每个文档微调其相关度评分 <code class="literal">_score</code> 的目的。</p>
<p>假设想要查询关于 “full-text search（全文搜索）” 的文档，但我们希望为提及 “Elasticsearch” 或 “Lucene” 的文档给予更高的 <em>权重</em> ，这里 <em>更高权重</em> 是指如果文档中出现 “Elasticsearch” 或 “Lucene” ，它们会比没有的出现这些词的文档获得更高的相关度评分 <code class="literal">_score</code> ，也就是说，它们会出现在结果集的更上面。</p>
<p>一个简单的 <code class="literal">bool</code> <em>查询</em> 允许我们写出如下这种非常复杂的逻辑：</p>
<div class="pre_wrapper lang-sense">
<pre class="programlisting prettyprint lang-sense">GET /_search
{
    "query": {
        "bool": {
            "must": {
                "match": {
                    "content": { <a id="CO63-1"></a><i class="conum" data-value="1"></i>
                        "query":    "full text search",
                        "operator": "and"
                    }
                }
            },
            "should": [ <a id="CO63-2"></a><i class="conum" data-value="2"></i>
                { "match": { "content": "Elasticsearch" }},
                { "match": { "content": "Lucene"        }}
            ]
        }
    }
}</pre>
</div>
<div class="sense_widget" data-snippet="snippets/100_Full_Text_Search/25_Boost.json"></div>
<div class="calloutlist">
<table border="0" summary="Callout list">
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO63-1"><i class="conum" data-value="1"></i></a></p>
</td>
<td align="left" valign="top">
<p><code class="literal">content</code> 字段必须包含 <code class="literal">full</code> 、 <code class="literal">text</code> 和 <code class="literal">search</code> 所有三个词。</p>
</td>
</tr>
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO63-2"><i class="conum" data-value="2"></i></a></p>
</td>
<td align="left" valign="top">
<p>如果 <code class="literal">content</code> 字段也包含 <code class="literal">Elasticsearch</code> 或 <code class="literal">Lucene</code> ，文档会获得更高的评分 <code class="literal">_score</code> 。</p>
</td>
</tr>
</table>
</div>
<p><code class="literal">should</code> 语句匹配得越多表示文档的相关度越高。目前为止还挺好。</p>
<p>但是如果我们想让包含 <code class="literal">Lucene</code> 的有更高的权重，并且包含 <code class="literal">Elasticsearch</code> 的语句比 <code class="literal">Lucene</code> 的权重更高，该如何处理?</p>
<p>我们可以通过指定 <code class="literal">boost</code> 来控制任何查询语句的相对的权重， <code class="literal">boost</code> 的默认值为 <code class="literal">1</code> ，大于 <code class="literal">1</code> 会提升一个语句的相对权重。所以下面重写之前的查询：</p>
<div class="pre_wrapper lang-sense">
<pre class="programlisting prettyprint lang-sense">GET /_search
{
    "query": {
        "bool": {
            "must": {
                "match": {  <a id="CO64-1"></a><i class="conum" data-value="1"></i>
                    "content": {
                        "query":    "full text search",
                        "operator": "and"
                    }
                }
            },
            "should": [
                { "match": {
                    "content": {
                        "query": "Elasticsearch",
                        "boost": 3 <a id="CO64-2"></a><i class="conum" data-value="2"></i>
                    }
                }},
                { "match": {
                    "content": {
                        "query": "Lucene",
                        "boost": 2 <a id="CO64-3"></a><i class="conum" data-value="3"></i>
                    }
                }}
            ]
        }
    }
}</pre>
</div>
<div class="sense_widget" data-snippet="snippets/100_Full_Text_Search/25_Boost.json"></div>
<div class="calloutlist">
<table border="0" summary="Callout list">
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO64-1"><i class="conum" data-value="1"></i></a></p>
</td>
<td align="left" valign="top">
<p>这些语句使用默认的 <code class="literal">boost</code> 值 <code class="literal">1</code> 。</p>
</td>
</tr>
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO64-2"><i class="conum" data-value="2"></i></a></p>
</td>
<td align="left" valign="top">
<p>这条语句更为重要，因为它有最高的 <code class="literal">boost</code> 值。</p>
</td>
</tr>
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO64-3"><i class="conum" data-value="3"></i></a></p>
</td>
<td align="left" valign="top">
<p>这条语句比使用默认值的更重要，但它的重要性不及 <code class="literal">Elasticsearch</code> 语句。</p>
</td>
</tr>
</table>
</div>
<div class="note admon">
<div class="icon"></div>
<div class="admon_content">
<a id="boost-normalization"></a>
<p><code class="literal">boost</code> 参数被用来提升一个语句的相对权重（ <code class="literal">boost</code> 值大于 <code class="literal">1</code> ）或降低相对权重（ <code class="literal">boost</code> 值处于 <code class="literal">0</code> 到 <code class="literal">1</code> 之间），但是这种提升或降低并不是线性的，换句话说，如果一个 <code class="literal">boost</code> 值为 <code class="literal">2</code> ，并不能获得两倍的评分 <code class="literal">_score</code> 。</p>
<p>相反，新的评分 <code class="literal">_score</code> 会在应用权重提升之后被 <em>归一化</em> ，每种类型的查询都有自己的归一算法，细节超出了本书的范围，所以不作介绍。简单的说，更高的 <code class="literal">boost</code> 值为我们带来更高的评分 <code class="literal">_score</code> 。</p>
<p>如果不基于 TF/IDF 要实现自己的评分模型，我们就需要对权重提升的过程能有更多控制，可以使用 <a class="xref" href="function-score-query.html" title="function_score 查询"><code class="literal">function_score</code> 查询</a>操纵一个文档的权重提升方式而跳过归一化这一步骤。</p>
</div>
</div>
<p>更多的组合查询方式会在下章<a class="xref" href="multi-field-search.html" title="多字段搜索">多字段搜索</a>中介绍，但在此之前，让我们先看另外一个重要的查询特性：文本分析（text analysis）。</p>
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